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Scope of Statistical Modeling and Optimization Techniques in Management Decision Making Process
Submission Deadline: Mar. 20, 2015

This special issue currently is open for paper submission and guest editor application.

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Lead Guest Editor
Department of Aplied Mathematics and Statistics, School of Science and Technology, The University of Fiji, Lautoka, Fiji
Guest Editors
  • Ram Bilas Misra
    Department of Applied Mathematics, State University of New York, Cobleskill, New York, USA
  • Bijay Singh
    Department of Soils Science, Punjab Agricultural University, Ludhiana, Punjab, India
  • Professor Chandra K. Jaggi
    Department of Operations Research, University of Delhi, New Delhi, India
  • Charanjeet Singh Arneja
    Department of Agricultural Extension, Punjab Agricultural University, Ludhiana, India
  • Professor Vijay Vir Singh
    Department of Mathematics and Statistics, Yobey State University, Yobe, Nigeria
  • Er. Avadhesh Kumar Maurya, B.Tech., M.Tech.
    Assistant Professor & Head, Department of Electronics & Communication Engineering, Lucknow Institute of Technology, Lucknow (U.P. Technical University, Lucknow, India), India
  • Dr. Syed Ghani, Ph.D.
    Acting Director, Centre for Climate Change, Energy, Environment and Sustainable Development (CCCEESD) The University of Fiji, Fiji, Fiji
  • Dr. Rajender Kumar Bathla, MCA, M.Tech., Ph.D.
    Senior Assistant Professor, Department of Computer Science & Engineering, Haryana Institute of Engineering and Technology, Kaithal (Kuruchhetra University, Kuruchhetra, India), India
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=146). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
1
Authors: Vishwa Nath Maurya
Pages: 1-10 Published Online: Mar. 11, 2015
DOI:
Views 3157 Downloads 188
2
Authors: Vishwa Nath Maurya, Ram Bilas Misra, Chandra K. Jaggi, Avadhesh Kumar Maurya
Pages: 11-18 Published Online: Mar. 11, 2015
DOI:
Views 3239 Downloads 195
3
Authors: Vishwa Nath Maurya, Ram Bilas Misra, Chandra K. Jaggi, Charanjeet Singh Arneja, Rama Shanker Sharma, Avadhesh Kumar Maurya
Pages: 19-26 Published Online: Mar. 11, 2015
DOI:
Views 2873 Downloads 181
4
Authors: Vishwa Nath Maurya, Rama Shanker Sharma, Saad Talib Hasson Aljebori, Avadhesh Kumar Maurya, Diwinder Kaur Arora
Pages: 27-32 Published Online: Mar. 11, 2015
DOI:
Views 3009 Downloads 268
5
Authors: Vishwa Nath Maurya, Chandra K. Jaggi, Bijay Singh, Charanjeet Singh Arneja, Avadhesh Kumar Maurya, Diwinder Kaur Arora
Pages: 33-43 Published Online: Mar. 11, 2015
DOI:
Views 3352 Downloads 415
6
Authors: Vishwa Nath Maurya, Madaki Umar Yusuf, Vijay Vir Singh, Babagana Modu
Pages: 44-51 Published Online: Mar. 21, 2015
DOI:
Views 2833 Downloads 166
Introduction
Statistical modeling and optimization techniques find vast applications in management decision making process. Whether designing new products, streamlining a production process or evaluating current vs. prospective customers, today’s business managers face greater complexities than ever before. Running a shop on instinct no longer suffices. Statistics provide managers with more confidence in dealing with uncertainty in spite of the flood of available data, enabling managers to more quickly make smarter decisions and provide more stable leadership to staff relying on them. Statistical analysis of a representative group of consumers can provide a reasonably accurate, cost-effective snapshot of the market with faster and cheaper statistics than attempting a census of very single customer a company may ever deal with. The statistics can also afford leadership an unbiased outlook of the market, to avoid building strategy on uncorroborated presuppositions. Statistics back up assertions. Leaders can find themselves backed into a corner when persuading people to move in a direction or take a risk based on unsubstantiated opinions. Statistics can provide objective goals with stand-alone figures as well as hard evidence to substantiate positions or provide a level of certainty to directions to take the company. Statistics can point out relationships. A careful review of data can reveal links between two variables, such as specific sales offers and changes in revenue or dissatisfied customers and products purchased. Delving into the data further can provide more specific theories about the connections to test, which can lead to more control over customer satisfaction, repeat purchases and subsequent sales volume. Anyone who has looked into continuous improvement or quality assurance programs, such as Six Sigma or Lean Manufacturing, understands the necessity for statistics. Statistics provide the means to measure and control production processes to minimize variations, which lead to error or waste, and ensure consistency throughout the process. This saves money by reducing the materials used to make or remake products, as well as materials lost to overage and scrap, plus the cost of honoring warranties due to shipping defective products.

Employing techniques from other mathematical sciences, such as mathematical modeling, statistical analysis, and mathematical optimization, operations research arrives at optimal or near-optimal solutions to complex decision-making problems. Because of its emphasis on human-technology interaction and because of its focus on practical applications, operations research has overlap with other disciplines, notably industrial engineering and operations management, and draws on psychology and organization science. Operations research is often concerned with determining the maximum (of profit, performance, or yield) or minimum (of loss, risk, or cost) of some real-world objective. Originating in military efforts before World War II, its techniques have grown to concern problems in a variety of industries. Operational research (OR) encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency, such as simulation, mathematical optimization, queuing theory and other stochastic-process models, Markov decision processes, econometric methods, data envelopment analysis, neural networks, expert systems, decision analysis, and the analytic hierarchy process. Nearly all of these techniques involve the construction of mathematical models that attempt to describe the system. Because of the computational and statistical nature of most of these fields, OR also has strong ties to computer science and analytics. Operational researchers faced with a new problem must determine which of these techniques are most appropriate given the nature of the system, the goals for improvement, and constraints on time and computing power.
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